Calibration before and after temperature rescaling.
In Table REF  we report ECE before and after calibration via temperature rescaling [1]} using in-distribution data for the models reported in Table REF .
We observe that calibration is substantially improved after temperature rescaling, however it does not affect the ranking between different methods. In particular, all adversarial training methods significantly improve the calibration error, and RLAT leads to the best calibration reaching \(1.3\%\)  ECE on CIFAR-10-C when combined with AugMix.
Moreover, we note that all the CIFAR-10 models are overconfident since their optimal calibration temperature is greater than 1 (mostly in the range from \(1.2\)  to \(1.5\) ).
On ImageNet-100, the picture is similar and RLAT also improves calibration. For example, ANT3x3 leads to 4.5% ECE after calibration while ANT3x3 combined with RLAT achieves 2.8% ECE.
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